Providing on-demand audience based on network

ABSTRACT

A device receives, from a client device, a first request associated with target audience criteria and a time constraint. Based on the target audience criteria, the device sends, to an information device, a network state request. In response to the network state request, the device receives, from the information device, a network state response including user data associated with user devices. Based on the network state response, the device determines a predicted network state, including predicted user data associated with the user devices, associated with the time constraint. Based on the predicted network state and the target audience criteria, the device determines a predicted quantity of user devices associated with both the target audience criteria and the time constraint. The device sends, to the client device, a first response based on the determination of the predicted quantity of user devices.

BACKGROUND

Organizations often use marketing communications to inform or persuadean audience. The audience may be targeted so that marketingcommunications are more likely to reach an intended audience.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of an overview of an example implementationdescribed herein;

FIG. 2 is a diagram of an example environment in which systems and/ormethods, described herein, may be implemented;

FIG. 3 is a diagram of example components of one or more devices of FIG.2;

FIG. 4 is a flow chart of an example process for setting up an on-demandaudience service;

FIG. 5 is a diagram of an example data structure relating to the exampleprocess shown in FIG. 4;

FIG. 6 is a flow chart of an example process for providing an on-demandaudience service; and

FIGS. 7A and 7B are diagrams of an example implementation relating tothe example process shown in FIG. 6.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The following detailed description of example implementations refers tothe accompanying drawings. The same reference numbers in differentdrawings may identify the same or similar elements.

An organization deciding how and when to communicate with its targetaudience may consider numerous factors when evaluating the cost-benefitratio of a marketing campaign. A business owner may wish to know howmany potential customers are likely to see, and notice, an advertisementbefore deciding to launch a campaign. The campaign may also be subjectto a time constraint, e.g., when relating to a holiday sale, electioncampaign, or perishable goods. Implementations described herein mayassist an individual or organization in communicating with a quantifiedand engaged target audience within a time constraint.

FIG. 1 is a diagram of an overview of an example implementation 100described herein. As shown in FIG. 1, example implementation 100 mayinclude an on-demand device. Example implementation 100 may furtherinclude an information device and a client device, such as a computer,tablet, or smart phone. The client device may present information on adisplay, such as a screen or touch screen.

As shown in FIG. 1, the client device may send, to the on-demand device,a request for information identifying a quantity of users meeting targetcriteria within a target time frame. The on-demand device may determine,based on receiving the request from the client device, what current andhistorical information, associated with users of a network, may berelevant to the client's request. Based on this determination, theon-demand device may then send, to the information device, a request forrelevant current and historical information about users of the network.Based on the request sent by the on-demand device, the informationdevice may retrieve, from memory and/or the network, current andhistorical information associated with users of the network and mayfurther send, to the on-demand device, a response including theretrieved current and historical information associated with users ofthe network. Based on the response, sent by the information device, theon-demand device may determine a level of association between users, whoare and/or will be active within the target time frame, and the client'starget criteria. Based on this determination, the on-demand device maysend, to the client device, a response indicating the quantity of activeusers predicted to meet the target criteria within the target timeframe. In this way, a potential advertiser may know, with somecertainty, the quantity of potential customers to be reached within acertain time frame before sending a targeted marketing communication.

FIG. 2 is a diagram of an example environment 200 in which systemsand/or methods, described herein, may be implemented. As shown in FIG.2, environment 200 may include a client device 210, an on-demand device220, an information device 230, a group of user devices 240-1, . . . ,240-N (N>1) (hereinafter referred to collectively as “user devices 240”and individually as “user device 240”), and a network 250. Devices ofenvironment 200 may interconnect via wired connections, wirelessconnections, or a combination of wired and wireless connections.

Client device 210 may include a device capable of receiving, generating,storing, processing, and/or providing information for communicatingrequests to initiate a communication campaign. For example, clientdevice 210 may include a communications and/or computing device, such asa mobile phone (e.g., a smart phone, a radiotelephone, etc.), a desktopcomputer, a laptop computer, a tablet computer, a handheld computer, agaming device, or a similar device. In some implementations, clientdevice 210 may receive information from and/or transmit information toanother device in environment 200. For example, client device 210 maytransmit a request to, and receive a response from, on-demand device220, for initiating a communication campaign.

On-demand device 220 may include one or more devices capable of storing,processing, and/or routing information for receiving and processingnetwork information to respond to communication campaign requests. Forexample, on-demand device 220 may include a server device or acollection of server devices. In some implementations, on-demand device220 may include a communication interface that allows on-demand device220 to receive information from and/or transmit information to otherdevices in environment 200. For example, on-demand device 220 mayreceive a request from, and transmit a response to, client device 210.Alternatively, or additionally, on-demand device 220 may transmit arequest to, and receive a response from, information device 230.

User device 240 may include a device capable of receiving, generating,storing, processing, and/or providing information for generating networkinformation and for receiving communications to effect a communicationscampaign. For example, user device 240 may include a communicationsand/or computing device, such as a mobile phone (e.g., a smart phone, aradiotelephone, etc.), a desktop computer, a laptop computer, a tabletcomputer, a handheld computer, a gaming device, or a similar device. Insome implementations, user device 240 may receive information fromand/or transmit information to another device in environment 200. Forexample, user device 240 may transmit user information, associated withuser device 240, to information device 230 and may receivecommunications, associated with client device 210, from on-demand device220.

Network 250 may include one or more wired and/or wireless networks. Forexample, network 250 may include a cellular network (e.g., a long-termevolution (LTE) network, a 3G network, a code division multiple access(CDMA) network, etc.), a public land mobile network (PLMN), a local areanetwork (LAN), a wide area network (WAN), a metropolitan area network(MAN), a telephone network (e.g., the Public Switched Telephone Network(PSTN)), a private network, an ad hoc network, an intranet, theInternet, a fiber optic-based network, a cloud computing network, and/ora combination of these or another type of network.

The number and arrangement of devices and networks shown in FIG. 2 isprovided as an example. In practice, there may be additional devicesand/or networks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIG. 2. Furthermore, two or more devices shown in FIG. 2 may beimplemented within a single device, or a single device shown in FIG. 2may be implemented as multiple, distributed devices. Additionally, oralternatively, a set of devices (e.g., one or more devices) ofenvironment 200 may perform one or more functions described as beingperformed by another set of devices of environment 200.

FIG. 3 is a diagram of example components of a device 300. Device 300may correspond to client device 210, on-demand device 220, informationdevice 230, and/or user device 240. In some implementations, clientdevice 210, on-demand device 220, information device 230, and/or userdevice 240 may include one or more devices 300 and/or one or morecomponents of device 300. As shown in FIG. 3, device 300 may include abus 310, a processor 320, a memory 330, a storage component 340, aninput component 350, an output component 360, and a communicationinterface 370.

Bus 310 may include a component that permits communication among thecomponents of device 300. Processor 320 may include a processor (e.g., acentral processing unit (CPU), a graphics processing unit (GPU), anaccelerated processing unit (APU), etc.), a microprocessor, and/or anyprocessing component (e.g., a field-programmable gate array (FPGA), anapplication-specific integrated circuit (ASIC), etc.) that interpretsand/or executes instructions. Memory 330 may include a random accessmemory (RAM), a read only memory (ROM), and/or another type of dynamicor static storage device (e.g., a flash memory, a magnetic memory, anoptical memory, etc.) that stores information and/or instructions foruse by processor 320.

Storage component 340 may store information and/or software related tothe operation and use of device 300. For example, storage component 340may include a hard disk (e.g., a magnetic disk, an optical disk, amagneto-optic disk, a solid state disk, etc.), a compact disc (CD), adigital versatile disc (DVD), a floppy disk, a cartridge, a magnetictape, and/or another type of computer-readable medium, along with acorresponding drive.

Input component 350 may include a component that permits device 300 toreceive information, such as via user input (e.g., a touch screendisplay, a keyboard, a keypad, a mouse, a button, a switch, amicrophone, etc.). Additionally, or alternatively, input component 350may include a sensor for sensing information (e.g., a global positioningsystem (GPS) component, an accelerometer, a gyroscope, an actuator,etc.). Output component 360 may include a component that provides outputinformation from device 300 (e.g., a display, a speaker, one or morelight-emitting diodes (LEDs), etc.).

Communication interface 370 may include a transceiver-like component(e.g., a transceiver, a separate receiver and transmitter, etc.) thatenables device 300 to communicate with other devices, such as via awired connection, a wireless connection, or a combination of wired andwireless connections. Communication interface 370 may permit device 300to receive information from another device and/or provide information toanother device. For example, communication interface 370 may include anEthernet interface, an optical interface, a coaxial interface, aninfrared interface, a radio frequency (RF) interface, a universal serialbus (USB) interface, a Wi-Fi interface, a cellular network interface, orthe like.

Device 300 may perform one or more processes described herein. Device300 may perform these processes in response to processor 320 executingsoftware instructions stored by a computer-readable medium, such asmemory 330 and/or storage component 340. A computer-readable medium isdefined herein as a non-transitory memory device. A memory deviceincludes memory space within a single physical storage device or memoryspace spread across multiple physical storage devices.

Software instructions may be read into memory 330 and/or storagecomponent 340 from another computer-readable medium or from anotherdevice via communication interface 370. When executed, softwareinstructions stored in memory 330 and/or storage component 340 may causeprocessor 320 to perform one or more processes described herein.Additionally, or alternatively, hardwired circuitry may be used in placeof or in combination with software instructions to perform one or moreprocesses described herein. Thus, implementations described herein arenot limited to any specific combination of hardware circuitry andsoftware.

The number and arrangement of components shown in FIG. 3 is provided asan example. In practice, device 300 may include additional components,fewer components, different components, or differently arrangedcomponents than those shown in FIG. 3. Additionally, or alternatively, aset of components (e.g., one or more components) of device 300 mayperform one or more functions described as being performed by anotherset of components of device 300.

FIG. 4 is a flow chart of an example process 400 for setting up anon-demand audience service. In some implementations, one or more processblocks of FIG. 4 may be performed by on-demand device 220. Additionally,or alternatively, one or more process blocks of FIG. 4 may be performedby another device or a group of devices separate from or includingon-demand device 220, such as client device 210, information device 230,and/or user device 240.

As shown in FIG. 4, process 400 may include receiving current networkstate information from user devices associated with a network (block410). For example, on-demand device 220 may receive the current networkstate information stored by information device 230, by, e.g., sending,to information device 230, a network state request and receiving, frominformation device 230, a network state response associated with thecurrent network state information. Current network state information mayinclude various information sent to/from and/or associated with a userdevice 240 and/or a user of a user device 240, such as, e.g., locationinformation, message information, search information, purchaseinformation, power state information, etc.

To respond to a network state request sent from on-demand device 220,information device 230 may collect information sent to/from and/orassociated with user devices 240. Information device 230 may obtain thisinformation by communicating with or more network devices of network250. Information device 230 may collect this information on-demand(e.g., in response to a request from on-demand device 210), periodically(e.g., according to a schedule), based on a triggering event (e.g., whenuser device 240 logs onto network 250, sends/receives a message,initiates a purchase, relocates, changes power state, etc.), and/or inreal time. Information device 230 may further store some or all of thecollected information, as current network state information, in memoryassociated with information device 230.

Location information may include, e.g., a geographic location,orientation, velocity, etc., determined using, e.g., user device 240'snetwork connectivity (e.g., a network address and/or proximity to aWi-Fi network and/or cell tower) and/or a component of user device 240(e.g., a global positioning system (GPS) receiver, accelerometer,gyroscope, etc.). On-demand device 210 may use location information,associated with a user device 240, to predict future locationinformation and determine, based on a location target criterion, whethera user of user device 240 is and/or will be associated with a targetaudience.

Message information may include, e.g., content (e.g., text, image,sound, video, etc.) and/or metadata (e.g., a sender identifier,recipient identifier, time stamp, etc.), associated with a message, sentto/from user device 240 using, e.g., e-mail, short message service(SMS), multimedia message service (MMS), telephone, video conference,etc. On-demand device 220 may use message information, associated with auser device 240 and/or a user of user device 240, to predict a needand/or interest of the user and determine, based on a need/interesttarget criterion, whether the user is and/or will be associated with atarget audience.

Search information may include, e.g., content (e.g., a search term,result, etc.) and/or metadata (e.g., a search engine identifier, timestamp, key words, description, quantity of time spent accessing aresult, etc.), associated with a search query, sent to/from user device240 including, e.g., a search for a resource located within and/orwithout memory/storage associated with user device 240 (e.g., performinga local file search, web-based search, and/or cloud-based search).On-demand device 220 may use search information, associated with userdevice 240 and/or a user of user device 240, to predict a need and/orinterest of the user and determine, based on a need/interest targetcriterion, whether the user is and/or will be associated with a targetaudience.

Power state information may include, e.g., whether user device 240, or acomponent associated with user device 240 (e.g., a display), iscurrently on/off/idle. Alternatively, or additionally, power stateinformation may include information associated with interaction betweenuser device 240 and a user of user device 240 by, e.g., detecting userinput or activities associated with user-interaction (e.g., playingaudio and/or video), etc. On-demand device 220 may use power stateinformation, associated with a user device 240, to predict whether theuser is and/or will be likely to interact with user device 240 when,e.g., user device receives a communication from on-demand device 220.

As further shown in FIG. 4, process 400 may include updating historicalnetwork state information based on the current network state information(block 420). For example, on-demand device 220 and/or information device230 may store historical user information based on, e.g., previouslyreceived network state information, described above with respect toblock 410. On-demand device 220 and/or information device 230 may, e.g.,append the current network state information to the historical networkstate information before, after, or concurrently with on-demand device220's request for and/or receipt of current network state informationfrom information device 230.

As further shown in FIG. 4, process 400 may include updating userprofile information based on the current and historical network stateinformation (block 430). For example, on-demand device 220 may usenetwork state information, associated with a user device 240, togenerate a user profile for a user associated with user device 240.On-demand device 220 may use, e.g., a statistical model, correlatingnetwork state information with characteristics associated with a user,to generate the user profile. Alternatively, or additionally, on-demanddevice 220 may generate the user profile based on input associated witha user, e.g., an explicit preference, opt-in, opt-out, etc. The userprofile may include, e.g., a historical and/or current location, powerstate, need, interest, etc. associated with the user and/or user device240. On-demand device 220 may use the user profile to predict, for aparticular time and/or time range, whether a user is and/or will beassociated with a target audience and/or likely to interact with acommunication sent to user device 240.

Likewise, on-demand device 220 may generate and/or store a clientprofile associated with a client using client device 210. The clientprofile may include client characteristics and/or client target audienceinformation which may be associated with characteristics included in auser profile (e.g., so that client profile characteristics may becompared to user profile characteristics to determine whether a user isassociated with a client's target audience).

In this way, on-demand device 220 may set up an on-demand audienceservice for implementing, at the request of a client device 210, anon-demand communication campaign associated with a target audience.

Although FIG. 4 shows example blocks of process 400, in someimplementations, process 400 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 4. Additionally, or alternatively, two or more of theblocks of process 400 may be performed in parallel.

FIG. 5 is a diagram of example data structures relating to the exampleprocess shown in FIG. 4. As shown in FIG. 5, process 400 may employ userdata repository 500. For example, on-demand device 220 and/orinformation device 230 may store, as current data included in user datarepository 500, current network state information associated with a userdevice 240 and/or a user of user device 240. The current data mayinclude, e.g., the most recent location, message, web page request,power state, etc. of user device 240. The current data may be stored as,e.g., a list of values corresponding to characteristics included in thecurrent network state information.

Alternatively, or additionally, on-demand device 220 and/or informationdevice 230 may store, as historical data included in user datarepository 500, historical network state information associated with auser device 240 and/or a user of user device 240. The historical datamay include, e.g., a past location, message, web page request, powerstate, etc. of user device 240. The historical data may be stored as,e.g., a time-series of historical network states and/or a time series ofcharacteristics included in the historical network state information.

Alternatively, or additionally, on-demand device 220 and/or informationdevice 230 may store, as profile data included in the user datarepository 500, a user profile associated with the user of user device240. The user profile may include information based on the currentand/or historical network state information and/or information based onuser input. As an example, current and historical data may includelocation information, associated with a user device 240, which describesa location of user device 240 at various times during a time period.Based on this location information, on-demand device 220 may determinewhere a user of user device 240 is likely to be at a particular time.Further, on-demand device 220 may infer other information about the userbased on the location information, such as where the user lives orworks, the user's preferred mode of transportation, schedule, etc.Likewise, on-demand device 210 may make determinations and/orinferences, based on other network state information, to generate a userprofile.

On-demand device 220 may use information stored in the user datarepository 500 to respond to a request from client device 210.Alternatively, or additionally, information device 230 may useinformation stored in the user data repository 500 to respond to arequest from on-demand device 220. In this way, on-demand device 220and/or information device 230 may use user data repository 500 to set upan on-demand audience service.

As also shown in FIG. 5, process 400 may employ client repository 510.For example, on-demand device 220 and/or information device 230 maystore, as current data included in client repository 510, current clientinformation associated with a client device 210 and/or a clientassociated with client device 210. The current data may include, e.g.,the most recent location, message, web page request, power state, etc.of client device 210—similar to that described with respect to user datarepository 500. Alternatively, or additionally, the current data mayinclude information associated with a current and/or recentcommunication campaign (e.g., message content, a time constraint, targetcriteria, etc.). The current data may be stored as, e.g., a list ofvalues corresponding to characteristics included in the current clientinformation. On-demand device 220 may use the current client informationto service current requests from a client device 210 to initiate andeffect a communication campaign.

Alternatively, or additionally, on-demand device 220 and/or informationdevice 230 may store, as historical data included in client repository510, historical client information associated with a client device 210and/or a client associated with client device 210. The historical datamay include, e.g., a past location, message, web page request, powerstate, etc. of client device 210—similar to that described with respectto user data repository 500. Alternatively, or additionally, thehistorical data may include information associated with one or more pastcommunication campaigns (e.g., message content, a time constraint,target criteria, etc.). The historical data may be stored as, e.g., atime-series of historical client information and/or a time series ofcharacteristics included in the historical client information.

Alternatively, or additionally, on-demand device 220 and/or informationdevice 230 may store, as profile data included in the client repository510, client profile information associated with the client using clientdevice 210. The client profile information may include information basedon the current client information, historical client information, and/orinformation based on input (e.g., an explicit preference, targetcriterion, billing rate, etc.). As an example, current/historical datamay include location information which identifies a location and/or acommunication campaign associated with the client. Based on thislocation information, on-demand device 220 may determine a predictedtarget location criterion to be included in the client profile. Further,on-demand device 220 may infer other client profile information based onthe current/historical information, such as identifying target criteriabased on keywords in a message associated with a communication campaign.Likewise, on-demand device 210 may make determinations and/orinferences, based on other client information and/or based oninformation contained within the user data repository 500, to generate aclient profile.

On-demand device 220 and/or information device 230 may use the currentand historical client information to maintain a record of current/futurerequests, campaigns, etc. Alternatively, or additionally, on-demanddevice 220 may use the current/historical client information, and/orinformation included in the user data repository 500, to determinepredicted client information associated with future requests, campaigns,etc. (e.g., by determining future target criteria and/or a future timeconstraint based on past target criteria, time constraint, network stateinformation, user profiles, etc.). In this way, on-demand device 220and/or information device 230 may use client repository 510 to set up anon-demand audience service.

FIG. 6 is a flow chart of an example process 600 for providing anon-demand audience service. In some implementations, one or more processblocks of FIG. 6 may be performed by on-demand device 220. Additionally,or alternatively, one or more process blocks of FIG. 6 may be performedby another device or a group of devices separate from or includingon-demand device 220, such as client device 210, information device 230,and/or user device 240.

As shown in FIG. 6, process 600 may include receiving a first request,associated with a network state prediction, from a client device (block610). For example, client device 210 may send, to on-demand device 220,a first request to initiate a communication campaign, which may prompton-demand device 220 to make a network state prediction. The firstrequest may include and/or identify various information associated witha target audience. Information associated with a target audience mayinclude various characteristics associated with members of the targetaudience, including, e.g., an age, gender, income, personal preference,location, activity history, habit, etc. Alternatively, or additionally,the first request may include and/or identify a time constraint. Thetime constraint may indicate a level of immediacy—e.g., within a certainnumber of minutes, hours, days, etc.—or the time constraint may indicatea time or time range in the future—e.g., a particular day, week,holiday, season, event, etc. The first request may also include otherinformation that on-demand device 220 may use to determine informationassociated with a target audience and/or time constraint—e.g., byidentifying information associated with client device 210 and/or contentof a communication intended for the target audience.

As further shown in FIG. 6, process 600 may include sending a networkstate request, based on the first request from the client device, to aninformation device (block 620). For example, on-demand device 220 maysend a network state request, requesting network state informationassociated with the first request, to information device 230. Therequested network state information may identify various characteristicsassociated with user devices 240 and/or users of user devices 240,including, e.g., location information, message information, searchinformation, purchase information, power state information, user profileinformation, or the like. The identified characteristics, included inthe network state request, may serve as a first filter of information,stored by information device 230, by identifying network stateinformation which is associated with the first request.

As further shown in FIG. 6, process 600 may include receiving a networkstate response, based on the network state request, from the informationdevice (block 630). For example, on-demand device 220 may receive, frominformation device 230, a network state response, based on the networkstate request sent by on-demand device 220. The network state responsemay include some or all of the various characteristics, associated withuser devices 240 and/or users of user devices 240, requested in thenetwork state request (e.g., the network state information identified asbeing associated with the first request). Information device 230 may useinformation received, from user devices 240 and/or one or more othernetwork devices associated with network 250, to generate the networkstate response. Additionally, or alternatively, information device 230may also use previously stored information, received from user devices240 and/or one or more devices associated with network 250, to generatethe network state response.

As further shown in FIG. 6, process 600 may include determining anetwork state prediction based on the network state response (block640). For example, on-demand device 220 may determine, based on thereceived network state response and the first request, a predictednetwork state for the time and/or time frame associated with the timeconstraint identified by the first request. On-demand device 220 maydetermine the predicted network state using, e.g., an empiricalpredictive model based on current and/or historical network stateinformation, user profile data, and/or other information stored byinformation device 230. For example, on-demand device 220 may use astatistical model that describes relationships between target criteriaand characteristics identified in network state information and/or userprofile information. On-demand device 220 may use the model (e.g., byapplying a regression analysis) to forecast the expected values ofcharacteristics, associated with the target criteria, of user devices240 for the time and/or time frame associated with the request.

The predicted network state may include various characteristics,associated with user devices 240 and/or users of user devices 240, thaton-demand device 220 determines to be likely for the time and/or timeframe associated with the time constraint identified by the firstrequest. For example, based on current and historical locationinformation contained within the network state response, on-demanddevice may determine, e.g., an expected trajectory and/or location of auser device 230 and/or a user of a user device 230 at a future timeand/or time frame. As another example, based on current and historicalsearch/browsing/purchase/message history information contained withinthe network state response, on-demand device may determine, e.g., anexpected need and/or interest of a user of a user device 230 at a futuretime and/or time frame.

On-demand device 220 may also determine a confidence level for thenetwork state prediction. The confidence level may indicate a level ofcertainty associated with the predictive model and/or predictedcharacteristics associated with the network state prediction. Theconfidence level may be expressed as, e.g., a numerical confidence value(e.g., a percentage, ratio, etc.), a confidence range (e.g., boundingvalues for a predicted value), a possible deviation value (e.g., astandard deviation or “plus or minus” value), and/or a limit (e.g., aminimum and/or worst-case predicted value) associated with a confidencelevel.

As further shown in FIG. 6, process 600 may include sending a firstresponse, based on the first request and the network state prediction,to the client device (block 650). For example, on-demand device 220 maysend, to client device 210, a first response indicating a quantity ofusers associated with the target audience, associated with a clientusing client device 210, and/or the time constraint. On-demand device220 may, e.g., match user characteristics determined from the firstrequest with user characteristics determined from the network stateprediction, to determine a quantity of users associated with a targetaudience for the time and/or time frame associated with first request.

For example, on-demand device 220 may rank and/or qualify/quantify acorrelation level associated with the matching of user characteristicsbetween the first request and the network state prediction. Likewise,on-demand device 220 may determine the quantity, and/or confidencelevel, of users associated with the target audience by, e.g.,determining whether a correlation level and/or rank, associated with auser, satisfies a threshold associated with a target criterion. Forexample, a predicted characteristic may be associated with a weighingfactor, and on-demand device 220 may determine a rank and/or correlationlevel based on a sum of products of the predicted characteristics andtheir respective weighing factors. On-demand device 220 may send, withthe first response, e.g., a histogram, or a quantity based on ahistogram, associated with the identified quantity of users and theircorrelation level. The histogram may represent an estimate of aprobability density function associated with characteristics included inthe predicted network state.

The first response may also include other information including, e.g.,an expiration time for the first response and/or pricing informationassociated with a communication sent to some or all of the userspredicted to match the client's target audience. For example, on-demanddevice 220 may determine the expiration time based on the time and/ortime frame associated with the first request. On-demand device 220 maydetermine pricing information based on various characteristicsassociated with the first request received from the client device 210and/or based on other stored information.

As further shown in FIG. 6, process 600 may include receiving, from theclient device, a second request, based on the first response, to effecta campaign (block 660). For example, on-demand device 220 may receive,from client device 210, a second request to effect a communicationcampaign. The second request may, e.g., indicate a quantity of usersdesired for the target audience. The second request may further include,e.g., a message (including, e.g., text, image, sound, etc.) fordistributing to the identified target audience and/or informationassociated with billing (including, e.g., an account identifier, creditcard information, etc.) a client associated with client device 210.Alternatively, or additionally, the second request may include some orall of the information included in the first request.

As further shown in FIG. 6, process 600 may include sending acommunication, based on the first and second requests, to a user device(block 670). For example, on-demand device may send a message, includedwith the first and/or second request, to a quantity, identified in thefirst and/or second request, of recipient user devices 240 of network250. On-demand device 220 may identify recipient user devices 240 basedon matching between user characteristics determined from the firstand/or second request with user characteristics determined from thepredicted network state. Alternatively, or additionally, on-demanddevice 220 may send, to information device 230, another network staterequest and receive, from information device 230, another network stateresponse to acquire current network state information. On-demand device220 may then identify recipient user devices 240 based on matchingbetween user characteristics determined from the first and/or secondrequest with user characteristics determined from the current networkstate information received from information device 230. Users of userdevices 240 may receive, from on-demand device 220, the message includedin the first and/or second request, from client device 210, at the timeand/or within the time frame identified by the time constraintidentified by the first and/or second request. User devices 240 maysend, to information device 230, feedback information associated with auser's receipt and/or response to the message from on-demand device 220.For example, information device 230 may receive, from user device 240,feedback information indicating, e.g., whether a user read, shared,deleted, etc. the message and/or whether the user performed an actionassociated with the message (e.g., purchasing a product, visiting alocation/website, etc.).

In this way, a client using client device 210 may determine, prior toeffecting a communications campaign, the quantity of users to be reachedby the communication at and/or within a given time frame.

Although FIG. 6 shows example blocks of process 600, in someimplementations, process 600 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 6. Additionally, or alternatively, two or more of theblocks of process 600 may be performed in parallel.

FIGS. 7A and 7B are diagrams of an example implementation 700 relatingto example process 600 shown in FIG. 6 for providing an on-demandaudience service. In example implementation 700, assume that a client,Sara (Sara's Sweets), has an excess inventory of cupcakes which musteither be sold or discarded by the end of the day. Assume further thatSara wishes to send an immediate communication, offering to sell excesscupcakes at a discount, to one hundred potential customers.

As shown in FIG. 7A, assume, for example implementation 700, that Saraprovides message input (using, e.g., an application or a portal) to herclient device 210 for starting a new campaign. The message input mayinclude a title (“Cupcake Sale!”), text (“TODAY ONLY! Cupcakes are $1each, now until closing at 9 pm. While supplies last.”), and/or an image(of, e.g., cupcakes). Sara's client device 210 may then generate a firstrequest, based on Sara's input, and send the first request to anon-demand device 220. The first request may include information based onSara's message input. Additionally, or alternatively, the first requestmay indicate target criteria for her audience (e.g., within five milesof Sara's Sweets, likely to purchase dessert before 9 pm, likescupcakes, has purchased from Sara's Sweets in the past, is able toreceive a message containing text/picture(s)) and/or a time constraintfor sending the message input to users identified based on the targetcriteria (e.g., within the next five minutes). The target criteriaand/or time constraint may be included in the first request, determinedfrom the message input, and/or determined based on information,associated with Sara's Sweets, previously stored by Sara's client device210 and/or on-demand device 220.

As further shown in FIG. 7A, assume, for example implementation 700,that on-demand device 220 received the first request, sent from Sara'sclient device 210. On-demand device 220 identifies, based on the messageinput, target criteria, and/or time constraint, network stateinformation associated with the first request. For example, networkstate information (e.g., associated with the characteristic of beingwithin five miles of Sara's Sweets) might include location informationassociated with user devices 240, such as GPS, network, Wi-Fi, etc.based location and accelerometer, gyroscope, etc. based velocity. Asanother example, network state information (e.g., associated with thecharacteristics of being likely to purchase dessert before 9 pm, likingcupcakes, and/or purchasing from Sara's Sweets in the past) mightinclude user history, such as recent searches for food/drink, foodand/or drink purchases, messages about food/drink, etc. Alternatively,or additionally, the identified network state information may includenetwork/device activity of user device 240, such as an on/off/sleepstate, user interaction level, connected/disconnected state, etc. fordetermining whether and/or when users are likely to be interacting withuser devices 240 when a message is sent. The network state informationrequest may also identify a time frame, based on the time constraintand/or other information included in the client's first request,associated with the requested network state information. For example,on-demand device 220 may identify a time frame of five minutes based onthe Sara's desired immediacy for starting her campaign.

As further shown in FIG. 7A, assume, for example implementation 700,that on-demand device 220 receives, from information device, a networkstate response based on the network state request. For example,information device 230 may continuously or periodically collectinformation associated with network 250 and/or user devices 240associated with network 250. Information device 230 may further usecollected current and/or historical information to provide the usercharacteristic information requested by on-demand device in the networkstate request.

As further shown in FIG. 7A, assume, for example implementation 700,that on-demand device 220 sends, to Sara's client device 210, a firstresponse, based on the network state response received from informationdevice 230. The first response may contain a predicted quantity of users(e.g., 387) which match the target criteria, a cost associated withsending a message to users (e.g., $0.055 per user), and/or an expirationtime for the prediction/cost (e.g., five minutes). The predictedquantity of users may be, e.g., a lower limit of a minimum guaranteedconfidence interval. The cost may be based on information associatedwith Sara's Sweets (e.g., a pre-arranged billing agreement), informationassociated with the first request (e.g., the message content, targetcriteria, and/or time constraint), and/or predetermined information.

As further shown in FIG. 7A, assume, for example implementation 700,that Sara receives, using Sara's client device 210, the first responsefrom on-demand device 220. Sara's client device 210 may displayinformation associated with the first response (e.g., the predictedquantity of users, cost per user, time until expiration, etc.). Clientdevice 210 may further provide for input to, e.g., confirm and/or modifyinformation associated with the first response (e.g., to indicate atarget audience of fewer than the predicted quantity of users). Assumethat Sara provides input to Sara's client device 210 indicating that shewishes to send her message to one hundred users.

As shown in FIG. 7B, assume, for example implementation 700, that Sara'suser device 210 sends, based on Sara's input and prior to the expirationtime indicated by the first response, a second request to on-demanddevice 220 confirming her request to send her message, immediately, toone hundred users, for a total price of $5.50. The second request maycontain a message based on the message input provided by Sara as well asthe quantity of users Sara wishes to reach.

As further shown in FIG. 7B, assume, for example implementation 700,that on-demand device receives the second request. Based on the secondrequest, on-demand device 220 may update the network state informationby sending, to information device 230, another network state request andreceiving, from information device 230, another network state response.Based on the network state information, received from information device230, on-demand device 220 may identify users and/or user devices 240,associated with network 250, which match the target criteria. On-demanddevice may further send a communication, based on the message includedin the first and/or second request, to some or all of the identifieduser devices 240, based on the first and/or second request (e.g., to thefirst 100 matching users or to the 100 highest-ranked matching users).

As further shown in FIG. 7B, assume, for example implementation 700,that each of the one hundred user devices 240 received, from on-demanddevice 220, the communication, containing the message. Each of userdevices 240 may display the communication, or an indication that acommunication is available to display, to users of user devices 240. Thecommunication displayed may include the message as well as otherinformation (e.g., directions to Sara's Sweets, an option to call Sara'sSweets, an option to share the communication, etc.).

As indicated above, FIGS. 7A and 7B are provided merely as an example.Other examples are possible and may differ from what was described withregard to FIGS. 7A and 7B.

Implementations described herein may assist a client in sendingon-demand communications to a predicted quantity of users associatedwith a network within a particular time frame.

The foregoing disclosure provides illustration and description, but isnot intended to be exhaustive or to limit the implementations to theprecise form disclosed. Modifications and variations are possible inlight of the above disclosure or may be acquired from practice of theimplementations.

As used herein, the term component is intended to be broadly construedas hardware, firmware, or a combination of hardware and software.

Some implementations are described herein in connection with thresholds.As used herein, satisfying a threshold may refer to a value beinggreater than the threshold, more than the threshold, higher than thethreshold, greater than or equal to the threshold, less than thethreshold, fewer than the threshold, lower than the threshold, less thanor equal to the threshold, equal to the threshold, etc.

Certain user interfaces have been described herein and/or shown in thefigures. A user interface may include a graphical user interface, anon-graphical user interface, a text-based user interface, etc. A userinterface may provide information for display. In some implementations,a user may interact with the information, such as by providing input viaan input component of a device that provides the user interface fordisplay. In some implementations, a user interface may be configurableby a device and/or a user (e.g., a user may change the size of the userinterface, information provided via the user interface, a position ofinformation provided via the user interface, etc.). Additionally, oralternatively, a user interface may be pre-configured to a standardconfiguration, a specific configuration based on a type of device onwhich the user interface is displayed, and/or a set of configurationsbased on capabilities and/or specifications associated with a device onwhich the user interface is displayed.

To the extent the aforementioned embodiments collect, store or employpersonal information provided by individuals, it should be understoodthat such information shall be used in accordance with all applicablelaws concerning protection of personal information. Additionally, thecollection, storage and use of such information may be subject toconsent of the individual to such activity, for example, through wellknown “opt-in” or “opt-out” processes as may be appropriate for thesituation and type of information. Storage and use of personalinformation may be in an appropriately secure manner reflective of thetype of information, for example, through various encryption andanonymization techniques for particularly sensitive information.

It will be apparent that systems and/or methods, described herein, maybe implemented in different forms of hardware, firmware, or acombination of hardware and software. The actual specialized controlhardware or software code used to implement these systems and/or methodsis not limiting of the implementations. Thus, the operation and behaviorof the systems and/or methods were described herein without reference tospecific software code—it being understood that software and hardwarecan be designed to implement the systems and/or methods based on thedescription herein.

Even though particular combinations of features are recited in theclaims and/or disclosed in the specification, these combinations are notintended to limit the disclosure of possible implementations. In fact,many of these features may be combined in ways not specifically recitedin the claims and/or disclosed in the specification. Although eachdependent claim listed below may directly depend on only one claim, thedisclosure of possible implementations includes each dependent claim incombination with every other claim in the claim set.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Furthermore,as used herein, the term “set” is intended to include one or more items,and may be used interchangeably with “one or more.” Where only one itemis intended, the term “one” or similar language is used. Also, as usedherein, the terms “has,” “have,” “having,” or the like are intended tobe open-ended terms. Further, the phrase “based on” is intended to mean“based, at least in part, on” unless explicitly stated otherwise.

1. A device comprising: one or more processors to: receive, from aclient device, a first request associated with an advertisementcampaign, the advertisement campaign including information associatedwith target audience criteria and time information for responding to anadvertisement associated with the advertisement campaign, the targetaudience criteria including information that identifies one or morecharacteristics of members of a target audience associated with theadvertisement; send, to an information device, a second request based onthe target audience criteria and the time information; receive, from theinformation device, a response including user data associated with userdevices, the user data being collected by the information device andincluding information that identifies respective locations of the userdevices, and at least one of: content or metadata of e-mail messagessent or received by the user devices, content or metadata of textmessages sent or received by the user devices, content or metadataassociated with search queries sent or received by the user devices,content or metadata associated with browsing activity of the userdevices, information associated with purchases initiated via the userdevices, or information indicating whether the user devices arepowered-on, powered-off, or idle; determine, based on the response,predicted network state information including predicted user dataassociated with the user devices and associated with the timeinformation for responding to the advertisement, the predicted user dataincluding predicted location information associated with each userdevice of the user devices, the predicted location informationidentifying an expected location of the user device at a time identifiedby the time information; determine, based on the predicted network stateinformation and the target audience criteria, a predicted quantity ofuser devices associated with both the target audience criteria and thetime information for responding to the advertisement, predicted locationinformation associated with each user device, of the predicted quantityof user devices, being used to determine that each user device, of thepredicted quantity of user devices, can be present at a particularlocation in accordance with the time information for responding to theadvertisement; and send, to the client device and based on determiningthe predicted quantity of user devices, a particular response, theparticular response being sent to the client device to identify aquantity of potential customers resulting from sending theadvertisement, the quantity of potential customers corresponding to thepredicted quantity of user devices.
 2. The device of claim 1, where theone or more processors are further to: receive, from the client device,a third request, based on sending the particular response, identifying aquantity of recipient user devices; and send a message, associated withthe first or third request, to the quantity of recipient user devicesidentified in the third request.
 3. The device of claim 1, where the oneor more processors, when determining the predicted network stateinformation, are to: determine information based on the user dataincluded in the response; and determine, using the information based onthe user data included in the response, the predicted network stateinformation.
 4. The device of claim 1, where the one or more processors,when determining the predicted quantity of user devices, are to:determine, based on the predicted user data and the target audiencecriteria, one or more correlation levels associated with one of the userdevices, the one or more correlation levels corresponding to a potentialmatch of the information associated with target audience criteria andthe information associated with the time information for responding tothe advertisement with the predicted network state information;determine whether each of the one or more correlation levels satisfies acorresponding threshold; and determine the predicted quantity of userdevices based on a quantity of the one or more correlation levels thatsatisfy the corresponding threshold.
 5. The device of claim 1, where theone or more processors, when determining the predicted quantity of userdevices, are to: determine, based on the user data and the predicteduser data, a plurality of user profiles, each user profile beingassociated with a corresponding user device of the user devices; anddetermine, based on a comparison between the target audience criteriaand each of the user profiles, the predicted quantity of user devices.6. The device of claim 1, where the one or more processors, whendetermining the predicted quantity of user devices, are to: determine aconfidence level associated with the predicted quantity of user devices,where the particular response is further based on the determinedconfidence level.
 7. The device of claim 1, where the one or moreprocessors, when determining the predicted quantity of user devices, areto: determine, based on the predicted user data, a predicted power stateassociated with one or more of the user devices, the predicted powerstate corresponding to: an on state, an off state, or an idle state; anddetermine, based on the predicted power state associated with the one ormore user devices, the predicted quantity of user devices.
 8. Anon-transitory computer-readable medium storing instructions, theinstructions comprising: one or more instructions that, when executed byone or more processors, cause the one or more processors to: receive,from a client device, a request associated with an advertisementcampaign, the advertisement campaign including information associatedwith target audience criteria and time information for responding to anadvertisement associated with the advertisement campaign, the targetaudience criteria including information that identifies one or morecharacteristics of members of a target audience associated with theadvertisement; receive, from one or more information devices associatedwith a network, network state information associated with user devices,the network state information being collected by the one or moreinformation devices and including information that identifies respectivelocations of the user devices, and at least one of: content or metadataof e-mail messages sent or received by the user devices, content ormetadata of text messages sent or received by the user devices, contentor metadata associated with search queries sent or received by the userdevices, content or metadata associated with browsing activity of theuser devices, information associated with purchases initiated via theuser devices, or information indicating whether the user devices arepowered-on, powered-off, or idle; determine, based on the network stateinformation, predicted network state information associated with theuser devices, the predicted network state information includingpredicted location information associated with the user devices andassociated with the time information for responding to theadvertisement, the predicted location information identifying anexpected location of each user device of the user devices at a timeidentified by the time information; determine, based on the predictednetwork state information, a predicted quantity of user devicesassociated with the request, predicted location information associatedwith each user device, of the predicted quantity of user devices, beingused to determine that each user device, of the predicted quantity ofuser devices, can be present at a particular location in accordance withsatisfy the time information for responding to the advertisement; andsend, to the client device, a response based on the predicted quantityof user devices to permit the advertisement campaign to be initiated,the response being sent to the client device to identify a quantity ofpotential customers resulting from sending the advertisement, thequantity of potential customers corresponding to the predicted quantityof user devices.
 9. The computer-readable medium of claim 8, where theinstructions further include: one or more instructions to identifywhether the user devices will satisfy the predicted network stateinformation, and the one or more instructions to determine the predictedquantity of user devices include: one or more instructions to determinethe predicted quantity of user devices based on identifying whether theuser devices will satisfy the predicted network state information. 10.The computer-readable medium of claim 8, where the one or moreinstructions to determine the predicted quantity of user devicesinclude: one or more instructions to identify the target audiencecriteria based on the request; and one or more instructions to comparethe predicted network state information and the target audiencecriteria, where the one or more instructions to determine the predictedquantity of user devices include: one or more instructions to determinethe predicted quantity of user devices based on comparing the predictednetwork state information and the target audience criteria.
 11. Thecomputer-readable medium of claim 8, where the one or more instructionsto receive the network state information include: one or moreinstructions to receive the network state information via an informationdevice, where the network state information comprises first informationand second information.
 12. The computer-readable medium of claim 8,where the one or more instructions to determine the predicted networkstate information include: one or more instructions to receive userprofile data associated with users of the user devices; and one or moreinstructions to determine, based on the user profile data, the predictednetwork state information.
 13. The computer-readable medium of claim 8,where the instructions further include: one or more instructions toreceive, from the client device, another request identifying a recipientquantity of user devices, where the recipient quantity of user devicesis based on the predicted quantity of user devices; and one or moreinstructions to send a message, based on the other request, to therecipient quantity of the user devices.
 14. The computer-readable mediumof claim 8, where the instructions further include: one or moreinstructions to update user profile data, associated with users of theuser devices, based on the network state information. 15-20. (canceled)21. A method comprising: receiving, by a device and from a clientdevice, a first request associated with an advertisement campaign, theadvertisement campaign including information associated with targetaudience criteria and information associated with time information forresponding to an advertisement associated with the advertisementcampaign the target audience criteria including information thatidentifies one or more characteristics of members of a target audienceassociated with the advertisement; sending, by the device and to aninformation device, a second request based on the target audiencecriteria; receiving, by the device and from the information device, aresponse including user data associated with user devices, the user databeing collected by the information device and including information thatidentifies respective locations of the user devices, and at least oneof: content or metadata of e-mail messages sent or received by the userdevices, content or metadata of text messages sent or received by theuser devices, content or metadata associated with search queries sent orreceived by the user devices, content or metadata associated withbrowsing activity of the user devices, information associated withpurchases initiated via the user devices, or information indicatingwhether the user devices are powered-on, powered-off, or idle;determining, by the device and based on the response, predicted networkstate information including predicted user data associated with the userdevices and associated with the time information for responding to theadvertisement, the predicted user data including predicted locationinformation associated with each user device of the user devices, thepredicted location information identifying an expected location of theuser device at a time identified by the time information; determining,by the device and based on the predicted network state information andthe target audience criteria, a predicted quantity of user devicesassociated with both the target audience criteria and the timeinformation for responding to the advertisement, predicted locationinformation associated with each user device, of the predicted quantityof user devices, being used to determine that each user device, of thepredicted quantity of user devices, can be present at a particularlocation in accordance with the time information for responding to theadvertisement; and sending, by the device and to the client device andbased on determining the predicted quantity of user devices, aparticular response, the particular response being sent to the clientdevice to identify a quantity of potential customers resulting fromsending the advertisement, the quantity of potential customerscorresponding to the predicted quantity of user devices.
 22. The methodof claim 21, further comprising: receiving, from the client device, athird request, based on sending the particular response, identifying aquantity of recipient user devices; and sending a message, associatedwith the first or third request, to the quantity of recipient userdevices identified in the third request.
 23. The method of claim 21,where determining the predicted network state information includes:determining information based on the user data included in the response;and determining, using the information based on the user data includedin the response, the predicted network state information.
 24. The methodof claim 21, where determining the predicted quantity of user devicesincludes: determining, based on the predicted user data and the targetaudience criteria, one or more correlation levels associated with one ofthe user devices, the one or more correlation levels corresponding to apotential match of the information associated with target audiencecriteria and the information associated with the time information forresponding to the advertisement with the predicted network stateinformation; determining whether each of the one or more correlationlevels satisfies a corresponding threshold; and determining thepredicted quantity of user devices based on a quantity of the one ormore correlation levels that satisfy the corresponding threshold. 25.The method of claim 21, where determining the predicted quantity of userdevices includes: determining, based on the user data and the predicteduser data, a plurality of user profiles, each user profile beingassociated with a corresponding user device of the user devices; anddetermining, based on a comparison between the target audience criteriaand each of the user profiles, the predicted quantity of user devices.26. The method of claim 21, where determining the predicted quantity ofuser devices includes: determining, based on the predicted user data, apredicted power state associated with one or more of the user devices,the predicted power state corresponding to: an on state, an off state,or an idle state; and determining, based on the predicted power stateassociated with the one or more user devices, the predicted quantity ofuser devices.